{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T10:25:06Z","timestamp":1777285506275,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T00:00:00Z","timestamp":1651536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["NRF-2018R1A5A1025137"],"award-info":[{"award-number":["NRF-2018R1A5A1025137"]}]},{"DOI":"10.13039\/501100003725","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["NRF-2018R1A5A1025137"],"award-info":[{"award-number":["NRF-2018R1A5A1025137"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The adoption of artificial intelligence in post-earthquake inspections and reconnaissance has received considerable attention in recent years, owing to its exponential increase in computation capabilities and inherent potential in addressing disadvantages associated with manual inspections. Herein, we present the effectiveness of automated deep learning in enhancing the assessment of damage caused by the 2017 Pohang earthquake. Six classical pre-trained convolutional neural network (CNN) models are implemented through transfer learning (TL) on a small dataset, comprising 1780 manually labeled images of structural damage. Feature extraction and fine-tuning TL methods are trained on the image datasets. The performances of various CNN models are compared on a testing image dataset. Results confirm that the MobileNet fine-tuned model offers the best performance. Therefore, the model is further developed as a web-based application for classifying earthquake damage. The severity of damage is quantified by assigning damage assessment values, derived using the CNN model and gradient-weighted class activation mapping. The web-based application can effectively and automatically classify structural damage resulting from earthquakes, rendering it suitable for decision making, such as in resource allocation, policy development, and emergency response.<\/jats:p>","DOI":"10.3390\/s22093471","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T08:26:35Z","timestamp":1651566395000},"page":"3471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Rapid Post-Earthquake Structural Damage Assessment Using Convolutional Neural Networks and Transfer Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5607-9307","authenticated-orcid":false,"given":"Peter Damilola","family":"Ogunjinmi","sequence":"first","affiliation":[{"name":"School of Architecture, Civil, Energy, and Environment Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea"}]},{"given":"Sung-Sik","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4226-7435","authenticated-orcid":false,"given":"Bubryur","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Robot and Smart System Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9205-3836","authenticated-orcid":false,"given":"Dong-Eun","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Architecture, Civil, Energy, and Environment Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"key":"ref_1","unstructured":"Lowes, L., DesRoches, R., Eberhard, M., and Parra-Montesinos, G. 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